An Artificial Neural Network Based Power Control strategy of Low-Speed Induction Machine Flywheel Energy Storage System

This study introduces a power control strategy of a flywheel energy storage system (FESS) based on an artificial neural network (ANN) as a current decoupling network to charge/discharge the flywheel for grid c onnected applications such as grid frequency support/control , power conditioning and UPS applications. The proposed system is a large-capacity low-speed FESS based on a field oriented controlled (FOC) squirrel cage induction machine. The controller is designed to avoid machine overloading while the flywheel is charged/discharged. Additionally, i t avoids using the required outer power loop or a hysteresis power controller, hence, simplifies the overall control a lgorithm. The validity of the developed control system is inve stigated via computer simulations using MATLAB/Simulink as well as experimental results. The proposed system is als o compared with conventional power control strategy with an additional outer power control loop to highlight th eir equivalence.

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